In today’s technological and business environment, data is at the core of every decision and insight. Big Data analytics is, in fact, a revolution in the information technology landscape that is projected to generate revenue of $103 billion by 2027. The necessity to gather, process, and convert data is increasing every day with large volumes of unstructured data collected from social media and the web.

Big data, however, cannot be equated with any particular volume of data. Terabytes, petabytes, and even exabytes of data collected over time may be used in big data deployments. To overcome the challenge of processing and analyzing vast datasets, open-source solutions for data science projects have emerged as robust solutions to tackle these data-related complexities. This extensive guide explores essential open-source tools for Data Science projects and offers an overview of seven of the most popular open-source big data tools.

Popular open-source data tools you can consider

The worldwide volume of data has grown exponentially, along with the rise of big data analytics. The big data analytics market is predicted to grow by over $655 billion by 2029. With this growing demand, organizations need to bring efficiency into data analytical tasks, which can be done with open-source data tools. Here are the essential open-source tools for data science projects you must know:

Also Read: Roles to explore after an MBA in Business Analytics

01. Apache Hadoop

Apache Hadoop is one of the most essential and must-consider open-source tools for big data projects. Apache Hadoop is a widely used tool in the big data business for large-scale data processing. Big data analytics relies heavily on Hadoop’s MapReduce programming model, which makes it possible to analyze structured and unstructured data in large batches efficiently.

Features:

• Distributed storage (Hadoop Distributed File System – HDFS)
• MapReduce programming model
• Scalability for big data processing

Uses:

• Analytics and processing of big data
• Data storage and archiving 
• Marketing analytics
• Risk management

Benefits:

• Terabytes of data can be processed in minutes and Petabytes in hours.
• A Hadoop cluster can be expanded simply by adding more nodes.
• The tool is significantly affordable since it is open source and stores data on commodity hardware.
• Replication of data from one Hadoop cluster to other nodes in the system protects against any hardware or software failure.

Project examples:

• Analysis of movie ratings for better movie recommendation
• Analysis of both structured and unstructured data to enhance consumer understanding and service.

02. Apache Spark

Apache Spark is one of the most popular open-source big data tools for analytics for big data workloads. Efficient in-memory data processing engine Apache Spark is renowned for its quick data analytics capabilities. Spark can operate independently, in the cloud, on Apache Hadoop, Apache Mesos, and Kubernetes—as well as against a variety of data sources.

Features:

• Swift processing with 100x faster in memory and 10x faster on the disk
• Dynamic in nature as it provides 80 high-level operators
• Fault tolerance
• Real-time stream processing

Uses:

• Used for real-time processing such as can real live tweets and perform analysis on them 
• Use in graph processing such as social or road networks
• The tool can be used to analyze a patient’s medical history and identify possible health issues

Benefits:

• It is developer-friendly, as it has a simple API that makes it easy to learn and use
• Can handle huge data sets
• Spark offers more than just “MAP” and “reduce.” It supports SQL queries, streaming data, graph algorithms, and machine learning (ML).

Project examples:

• It can predict flight delays

Also Read: Industries You Can Consider after an MSc in Business Analytics

03. Apache Flink

Apache Flink is a powerful open-source stream processing framework that has garnered significant popularity in the big data field in recent years. Large volumes of streaming data can be processed and analyzed in real-time, which makes it a desirable option for modern applications like machine learning, stock market analysis, and fraud detection.

Features:

• Processing capabilities for streams and batches
• Real-time processing
• Sophisticated state management

Uses:

• Used in event-driven applications that automate response to events as they happen and used in industries like finance, healthcare, and more
• Used in business process monitoring and fraud detection

Benefits:

• With very little latency, Apache Flink can scale to thousands of nodes.
• APIs for creating Java and Scala streaming applications are supported by Apache Flink.
• Due to its distributed runtime engine, Apache Flink is an excellent option for mission-critical streaming applications since it guarantees fault-tolerant stream processing and high availability.
• Multiple data formats, including CSV, JSON, Apache Parquet, and Apache Avro, are supported by Apache Flink.

Project examples:

• Examining transactional data and setting up alarms in response to suspicious activity.
• Examining log data to spot possible breaches of security early and take preventative action against cyberattacks.

04. Apache Kafka

Apache Kafka is one of the most essential open-source tools for data science projects and building real-time streaming data pipelines. It is a framework for event streaming that is used to gather, process, store, and integrate data at scale. Apache Kafka facilitates the creation of practical data pipelines for enterprises. To handle the data, it operates as a distributed system of clients and servers that supports the publish-subscribe model.

Features:

• It can process and store events.
• It is possible to integrate Kafka with other systems like AWS, Hadoop, Spark, and Storm.
• It can be scaled and is resistant to errors.
• It provides quicker processing.

Uses:

• Tracking website activities
• Monitoring IoT devices
• Managing supply chains
• Real-time data analytics

Benefits:

• Kafka can readily handle the massive volume of data streams
• It is reliable since it is distributed, partitioned, replicated, and fault-tolerant
• Kafka is incredibly quick and ensures that no data is lost or interrupted

Project Examples:

• Kafka is used by the LinkedIn tech stack for metrics monitoring, activity tracking, and message exchange. 
• It is used by Google Analytics to process data in real-time.

05. Jupyter Notebooks

It is a must-consider open-source tool for big data projects. With Jupyter Notebook, users can create and share narrative text documents, equations, formula visualizations, and live code. For data scientists and analysts, its interactive features are quite helpful.

Features:

• Accommodates a wide range of programming languages
• Combines text, graphics, and code into a single document
• Interactive and collaborative notebook interface

Uses:

• Exploratory data analysis (EDA)
• Data cleaning and transformation
• Data visualization
• Statistical modeling
• Machine learning

Benefits:

• Outstanding for presenting and exchanging data science work
• Incredibly flexible and engaging
• Ideal for investigating and analyzing data
• Integration with a range of libraries for data visualization

06. Apache Cassandra

A distributed database management system called Cassandra is made to manage large amounts of structured data among commodity computers. The Apache non-profit organization is in charge of this NoSQL data storage system, which uses a distributed design to provide high availability, scalability, and reliability.

Features:

• Rather than being a centralized system, Cassandra is well-distributed and designed to operate across several nodes.
• Cassandra scales elastically. That means there won’t be much resistance or trouble when scaling it up or down.
• The Cassandra Query Language, or CQL, was introduced by Cassandra. It’s an easy interface to access Cassandra.

Uses:

• IoT data management
• Social media data management
• Authentication and fraud detection
• Logistics and asset management

Benefits:

• It can deliver fast and consistent results
• It is free and can be modified by anyone
• It is flexible and can accept different types of data and schema

Project examples:

• Cassandra is used by Twitter for analytics
• It serves as Mahalo’s main near-time data storage facility

Also Read: Popular sports where data science is utilized extensively!

07. Hive

A tool for handling structured data kept in a Hadoop Distributed File System is Apache Hive. It runs on Apache Hadoop and simplifies querying and analysis. For users who are familiar with SQL, it provides an interface for querying data that is similar to SQL (HiveQL).

Features: 

• Hive Query Language allows several users to query the data simultaneously
• Apache Hive entirely fulfills Apache Hadoop’s low-level interface requirement
• Integration with tools from the Hadoop ecosystem

Uses:

• Information warehousing
• Quick data analysis
• Managing, writing, and accessing massive files stored in dispersed storage

Benefits: 

• With its Gantt charts, it facilitates the maintenance of precise project timetables and increases productivity through task automation.
• It is a cheaper option.
• In addition to analyzing data, it gives users the ability to read and write data in a systematic way.

Project examples:

• Real-time queries and analytics
• Data processing and transformation

MAHE: Learn in-demand tools to become industry-ready!

Big Data is the cornerstone of artificial intelligence, machine learning, and analytics in data science. It is essential for making wise judgments and promoting corporate expansion. It assists businesses in streamlining their processes, cutting expenses, and increasing productivity.

To become an expert in data science or business analytics, consider opting for an online MSC data science or business analytics from the Manipal Academy of Higher Education. The curriculum gives you the ideal balance of machine learning, big data analytics, and statistics to help you become proficient in using real-world data to solve issues. With MAHE’s effective learning pedagogy, world-class faculty, advanced digital learning platform, and more, students can unleash their potential and open a gateway to a flourishing career in data science.

Conclusion

Big data in data science is the key to releasing the potential of data and transforming it into insights that can be put to use for future growth. To put data science into action, with the use of data science open-source tools, organizations may fully utilize their data, fostering innovation and evidence-based decision-making. These solutions give users the ability to find new opportunities, optimize operations, detect patterns, and gain insights from huge databases. 

These free and effective big data tools for data science can help analysts in extracting valuable insights from the data. These tools, each possessing unique characteristics and functions, are essential components of open-source big data solutions. You can learn all these tools and more with MAHE and steer towards a rewarding career.

Data science is a discipline that extracts usable insights from data by combining domain knowledge, computing abilities, and maths and statistics knowledge. Numbers, text, images, video, audio, and other data are utilised to construct artificial intelligence (AI) systems that can perform tasks that would ordinarily need human intelligence. 

Data science demand in India is growing day by day and many graduates in data science are beginning to form a revolution in this industry. As a result, the industry produces insights that analysts and business users can employ to create meaningful commercial value.

What caused the data analytics boom?

Businesses are generating, storing, selling, and sharing more data than they have ever before. Everything a company does is driven by customer needs, and organisations need data to make informed decisions.

“In the past, data was obtained by individuals,” said Stephen Beyer, PhD, department chair of Purdue University Global’s School of Business and Information Technology. Machines do not sleep when people do. As a result, people who can handle and make sense of this massive amount of data are needed in nearly every aspect of life.”

Quick read: Best resources to master data science

Data analytics: the absolute essential in today’s business world

As technology’s influence in many areas grows, vast amounts of data are generated that might provide useful insights into the subject. This has resulted in a growth in the data sector over the previous ten years.

Data gathering, on the other hand, must be supported with data analysis in order to get decision-making insights. Data analytics aids firms and sectors in making sense of massive amounts of data in order to expand and thrive. Investing in data analytics solutions can mean the difference between a successful company now and one that fails in the future.

How has the demand for data science changed after COVID-19?

In just the first quarter of 2020, data science jobs and the need for data science have increased by 56%. One reason for this was an increase in the industry’s desire to utilise data that was already available. Increased demand is also due to the trustworthiness of data sources and the effectiveness of analysis. It’s also due to the widespread availability of technology.

Data science jobs in Bangalore are increasing day by day and so are the data science jobs in India. The below-mentioned points are a few of the reasons why data science demand in India is on the rise. 

• Fierce competition

It is not necessary for your next data science employment to be located in your current city. You’ve been able to explore new pathways in data science and collaborate with multinationals outside of your geographic region thanks to remote working and virtual recruiting.

This naturally raises competitiveness, and you’re suddenly up against someone with more experience and expertise from a different place. This is especially true for newer employees who are exposed to more competition.

On the other hand, you can apply for employment outside of India and earn higher pay. The average compensation of Indian data scientists is far lower than that of American data scientists, but you may close the gap now.

• Easy to upskill

In a sector as dynamic and changing as data science, upskilling is a must. As more data scientists seek the same job post-pandemic, the demand for data scientists with diverse talents has skyrocketed.

Companies have additional options because of layoffs, and you’ll be competing with upskilled individuals who have obtained numerous certificates even before applying for the job. According to a LinkedIn poll, around 64% of employees have improved their skills as a result of the lockout.

And because firms no longer offer training programmes or in-person courses, the entire learning process is now on you. You won’t be able to adjust to the job as quickly or seamlessly if you work from home, but mastering those skills online will assist.

You should ideally enrol in a course to gather information, which you can subsequently apply to numerous settings.

Skills needed for data science need to be studied thoroughly by data science graduates, who look to establish a strong career in this field. 

• Demand for contract-based hiring

Because firms are now depending on cost-cutting tactics, contract-based hiring has expanded post-covid. The number of freelancers hired has increased, as has the number of contract workers hired. Companies would be able to engage data scientists and analysts on a contract basis if they couldn’t afford to pay them well.

Data science demand in India constitutes contract-based hiring. The tendency is expected to continue until the economy picks up and the workflow improves. Contractual hiring is unquestionably advantageous to businesses. Unlike salaried employees, who must be paid even when there is a labour shortage, they only pay for what they use.

The trend has spread to positions in data science and analytics. Some companies may still consider these occupations to be secondary, and hence desire to save money by decreasing this department’s budget.

Benefits of a data science career

Are you wondering: Is Data Science a good career option for you?

Data science job opportunities are plenty in number and so are the benefits of data science careers. 

• The demand factor

The field of data science is extremely popular. With 11.5 million new jobs predicted to be generated by 2026, it is the fastest-growing job on Linkedin. 

• Abundance of positions

Only a few people have all of the skills required to be a full-fledged Data Scientist. As a result, Data Science has a lower saturation rate than other IT disciplines.

• A highly paid career

One of the highest-paying careers is data science. According to Glassdoor, data scientists make an average of 10,00,000 a year in India. As a result, Data Science is a highly profitable career path. Not to mention, data science freelance jobs, which are on the rise post-COVID-19. 

• Data science is versatile

Data Science has a wide range of applications. In the healthcare, banking, consulting, and e-commerce industries, it is widely used. 

• Product factor

Machine Learning has helped the industry to build better goods tailored specifically for user experiences thanks to Data Science.

E-commerce websites, for example, use Recommendation Systems to present consumers with customised insights based on their previous purchases. 

Scope of data science in India

In a developing country like India, there are plenty of places for data scientists, data analysts, big data engineers, big data managers, and data architects.

Becoming a data scientist has been a lucrative career option for the past several years. A variety of other factors, in addition to the growing relevance of data and the need to improve it, have driven the demand for data scientists in India.

With data being dubbed the “next oil” for businesses, analytics has evolved into a means of extracting useful information. The future scope of data science is being driven by the potent combination of both. Organisations all across the world are coming up with new methods to harness data and use it to advance their operations.

Why should you acquire data-driven decision-making skills?

Businesses can improve their performance by using data-driven decision-making to generate real-time insights and projections. They can then put various methods to the test and make well-informed company decisions for long-term growth.

There are a slew of reasons why using data to make decisions should be at the forefront of every modern company’s culture, and we’ll go through the most important ones.

• Organisational growth

The characteristics of data in decision-making are consistency and constant growth. Companies may zero in on crucial insights based on a variety of functions, processes, and departmental activities using data-driven decision-making.

One data decision after another, executed consistently, will enable you to establish actionable benchmarks that lead to continuous development and growth, which are critical components for long-term success in today’s cutthroat digital world.

• Innovation and knowledge

Companies are made or broken by data-driven business decisions. This demonstrates the value of online data visualisation for decision-making.

Companies that use a collaborative approach to decision-making are more likely to treat information as a valuable asset than companies that take other, more ambiguous approaches. 

You’ll also build a culture of data-driven education – a commercial ecosystem where everyone uses the power of information to learn more while working to the best of their ability – if you see digital insights as a genuine advantage.

• New business opportunities

Data-driven decision-making results in the discovery of new and interesting business prospects. Drilling down into accessible visual data will provide you with a panoramic perspective of your company’s main activities, allowing you to make a series of sound decisions that will enhance your company’s commercial progress.

You’ll discover possibilities to increase your progress, make new professional contacts, and build ideas that will provide you a significant competitive advantage armed with the deep-dive insights that will improve your judgement.

• Better communication

Working with a data-driven decision-making approach will help you become a better leader, which will spread throughout the organisation.

Working with compelling KPIs and visualisations will improve communication across the board, whether you’re talking about data-driven finance, a data-driven sales strategy, or any other form of insight-driven endeavour.

Every one of your departments will acquire the capacity to easily exchange insights and collaborate on crucial plans as a result of operating as one unified data-driven unit, making you more intelligent (and profitable) as a company.

• Adaptability

Last but not least, one of the most significant advantages of data-driven decision making is that it allows your company to be extremely adaptive.

You may build and evolve your empire over time by adopting digital data, making your company more versatile as a result. To keep up with the ever-changing scene around you, you must use data to make more educated and strong data-driven business decisions.

Data-driven decision-making tools can help you stay on top of developing trends and patterns that affect not only your internal operations but also the industry at large. If you can gain a deeper understanding of these trends or patterns, you’ll be able to make informed judgments that will keep you competitive, relevant, and lucrative at all times.

Data science opportunities

Must read: Eight easy steps to begin your first data science project

Data science is one of the most promising and in-demand occupations for qualified experts. A data science profession is both rewarding and lucrative, but it is not simple to break into the area. Working in data science does not necessitate a bachelor’s or master’s degree. What’s needed is the right set of skills and knowledge.

• Data analyst

Data analysts are responsible for a wide range of duties, including data visualisation, munging, and processing. They must also run queries against databases from time to time. Optimisation is one of a data analyst’s most significant talents. This is due to the fact that they must develop and modify algorithms that can be utilised to extract data from some of the world’s largest databases without causing data corruption.

• Data engineers

Data engineers create and test scalable Big Data ecosystems for businesses so that data scientists may run their algorithms on robust and well-optimised data platforms. To boost database performance, data engineers also update existing systems with newer or improved versions of current technologies.

• Data administrator

A database administrator’s job description is fairly self-explanatory: they are responsible for the proper operation of all of an enterprise’s databases and give or revoke services to corporate personnel based on their needs. They’re also in charge of database backups and restores.

Renowned companies recruiting data enthusiasts

Data scientists are in demand now and these are the top companies that hire data scientists.

• Slack
• Groundtruth
• Datazymes
• LoginRadius
• Zycus
• MAQ Software
• Acquia
• HCL Technologies
• NVIDIA
• Quartic.ai

Online MSc in Data Science is best done with MAHE: Here’s why!

MAHE’s Online MSc in Data Science stands out for its comprehensive curriculum, blending theory with hands-on experience. The program offers flexibility for remote learning while maintaining the institution’s renowned standards. MAHE’s faculty, comprised of industry experts, ensures a high-quality education, and the cutting-edge technology used enhances the online learning experience. With a strong focus on practical applications, industry relevance, and research, MAHE’s Online MSc in Data Science provides a well-rounded education, making it an optimal choice for those seeking a flexible yet rigorous online master’s program in this dynamic field.

Conclusion

Data science demand in India isn’t going away anytime soon. However, the profession is changing, and corporations are beginning to search for people who can solve problems using data.

This does not only apply to model making. When presented with a business problem, developing a machine learning model isn’t always the best solution.

A simple SQL query or visualisation may be all you need to know in some cases. Using machine learning approaches to solve every data problem is costly and time-consuming, which is why you need to know more. As long as you continue to study and build data-driven solutions, there is no automated tool that can replace a data scientist’s skill set.

MAHE offers online graduate, post-graduate and certificate programs designed perfectly for you. Enrol now!